Computational Collective Intelligence (CCI) is most often understood as
an AI sub-field dealing with soft computing methods which enable making
group decisions or processing knowledge among autonomous units acting in
distributed environments. Web-based systems, social networks and
multi-agent systems very often need these tools for working out
consistent knowledge states, resolving conflicts and making decisions.

Knowledge management systems

1Knowledge
Management System (KM System) refers to a (generally IT based) system
for managing knowledge in organizations for supporting creation,
capture, storage and dissemination of information. It can comprise a
part (neither necessary or sufficient) of a Knowledge Management
initiative. The idea of a KM system is to enable employees to have ready
access to the organization's documented base of facts, sources of
information, and solutions. For example a typical claim justifying the
creation of a KM system might run something like this: an engineer could
know the metallurgical composition of an alloy that reduces sound in
gear systems. Sharing this information organization wide can lead to
more effective engine design and it could also lead to ideas for new or
improved equipment.1 from Wikipedia

Agents and multi-agent systems

Agents and multi-agent systems are related to the modern software which
has long been recognized as a promising technology for constructing
autonomous, complex and intelligent systems. A key development in the
field of agent and multi-agent systems has been the specification of
agent communication languages and formalization of ontologies. Agent
communication languages are intended to provide standard declarative
mechanisms for agents to communicate knowledge and make requests of each
other, whereas ontologies are intended for conceptualization of the
knowledge domain. In this paradigm cognitive agents of heterogeneous
nature possess diverse conceptual views and ontologies the problem of
semantic mismatch arises, and a special conflict resolution strategies
based on computer-supported negotiation are necessary.

Recommendation and personalization in web systems

The main aim
of the recommendation systems is to deliver customized (personalized)
information to a very differentiated users. They may be applied in a
great variety of domains, such as: net-news filtering, web recommender,
personalized newspaper, sharing news, movie recommender, document
recommender, information recommender, e-commerce, purchase, travel and
store recommender, e-mail filtering, music recommender, student courses
recommender, user interface recommendation, negotiation systems, etc..
We consider two dimensions of the recommendation systems, user modeling
and user model exploitation. The former considers user profile
representation&maintenance and profile learning techniques. The
later contains information filtering method, matching techniques and
profile adaptation technique.

Ensemble and hybrid models of computational intelligence

Ensemble learning is a type of machine learning that studies algorithms and architectures that build
collections, or ensembles, of statistical classifiers/regressors that
are more accurate than a single classifier/regressor. This technique combine the output of machine learning
algorithms, called “weak learners”, in order to get smaller prediction
errors (in regression) or lower error rates (in classification). The
individual estimator must provide different patterns of generalization,
thus in the training process diversity is employed. Otherwise, the
ensemble would be composed of the same predictors and would provide as
good accuracy as the single one. It has been proved that the ensemble
performs better when each individual machine learning system is accurate
and makes errors on different examples. To the methods of ensemble learning we may include bagging,
boosting, stacking, subsampling, random subspaces, mixture of experts, and others.

Semantic Information Retrieval

Traditional
Information Retrieval (IR) methods are roughly adequate for modern Web
search and analysis. We focus on IR methodologies for current (Web 2.0)
or even future (Web 3.0) search and analysis engines. The techniques
range from link structure analysis to using social network relationship
semantics. We use and research paradigms and technologies like:

•Semantic Web (OWL)

•Linked Data (RDF. SPARQL)

•Web ontologies (FOAF)

•Web data aggregation

Multimedia Information Processing

Since
its early days hypertext has been used in association with multimedia
(hypermedia), therefore different types of multimedia information are
key ingredients of Web-based information systems. Our research covers
the following aspects of the information processing:

•Audio signal processing

•Image recognition and video clustering

•Lossy and lossless compression

•Platform independent playback support

System Performance Analysis and Improvement

System
performance and responsiveness are usually crucial issues for users,
especially in Web environment. Constant system development should always
be led in parallel with performance analysis. Our research in the field
covers:

•Content caching techniques

•Usability testing

•Content indexing algorithms

•Web-based optimization techniques and best practices

E-Learning Methodologies

Modern
e-learning (2.0) focuses mostly on Computer Supported Collaborative
Learning (CSCL). Using Moodle (StOPKa3) as a primary tool for teaching
is a great incentive for us for exploring new techniques and
applications of online collaboration. Research areas in this field
include: